By reading this article you can solve the below UPSC PYQs–
Explain the difference between computing methodology of India’s gross domestic product (GDP) before the year 2015 and after the year 2015.
(2021, GS -3, ECONOMY)
Why in the News
- The International Monetary Fund (IMF) recently assigned a ‘C’ grade—the second lowest category—to India’s national accounts statistics, which include the Gross Domestic Product (GDP) and Gross Value Added (GVA). T
- This grading coincided with the release of India’s Quarter 2 (Q2) data, which showed an impressive 8.2% growth.
- The grade signifies serious concerns expressed by the IMF regarding the methodology employed by India for calculating its growth data.
Background and Context
What Are National Accounts Statistics?
- National accounts statistics constitute comprehensive data sets utilized for measuring and analyzing economic performance of a nation.
- These statistics primarily include Gross Domestic Product (GDP) and Gross Value Added (GVA), which serve as fundamental indicators of economic growth and sectoral contributions.
- The reliability and accuracy of these measurements directly impact policy formulation, investment decisions, and international economic assessments.
IMF’s Grading System and Implications
- The International Monetary Fund’s grading represents an evaluation of data quality and methodology used in calculating national accounts.
- The assignment of a ‘C’ grade—indicating second lowest quality—suggests that significant methodological concerns exist regarding India’s approach to economic data compilation.
- Implications of IMF assessment are significant and multi-dimensional:
- International credibility of Indian economic data may be adversely affected, influencing trust placed by global institutions and partners.
- Policy formulation and evaluation may be weakened when based on growth figures affected by methodological uncertainty.
- Investment decisions and economic planning may face risks due to reliance on potentially misestimated growth trends.
- Comparative international economic analysis may be distorted, reducing accuracy of cross-country growth comparisons.
India’s GDP Calculation Methodology (2015)
India undertook a major revision of its Gross Domestic Product (GDP) estimation methodology in 2015, aimed at aligning national accounting practices with international standards and improving the comprehensiveness of economic measurement.
Pre-2015 GDP Calculation Method
- Basis of Calculation: GDP was measured at factor cost, focusing on income earned by factors of production such as land, labour, capital, and entrepreneurship.
- Base Year: 2004–05 was used as the base year for constant price estimates and deflators.
- Data Sources: Heavy reliance on the Annual Survey of Industries (ASI), with limited direct data on the unorganised sector.
- Methodological Framework: Followed the 1993 System of National Accounts (SNA) guidelines.
- Coverage Limitations: Inadequate inclusion of financial intermediation services and incomplete measurement of informal sector activities.
Post-2015 GDP Calculation Method
- Shift in Measurement: GDP began to be calculated at market prices, incorporating indirect taxes and subsidies to better reflect economic value.
- Revised Base Year: Updated to 2011–12 to capture structural changes in the economy.
- Updated Framework: Adoption of the 2008 UN System of National Accounts (SNA) for enhanced international comparability.
- Expanded Data Sources: Introduction of the MCA21 database for improved coverage of the corporate sector.
- Improved Sectoral Coverage: Enhanced inclusion of the financial sector and better representation of unorganised enterprises.
Key Developments under the New Methodology
Broader Data Sources
- Integration of data from the Ministry of Corporate Affairs.
- Use of statutory filings to capture private corporate sector activity.
Better Sectoral Coverage
- Inclusion of financial intermediation services within the services sector.
- Improved representation of the informal sector through surveys and indirect estimates.
Quality Enhancements
- Adoption of advanced techniques for estimating value addition across sectors.
- Improved estimation of government services and non-profit institutions.
However, despite aligning India’s GDP estimation framework with global best practices, fundamental challenges continue, especially in accurately capturing informal sector activity and in generating reliable real-time data during periods of economic disruption.
Core Issue: India’s GDP Calculation Methodology
Use of Organised Sector as Proxy
- India employs a problematic methodology in calculating Gross Domestic Product growth.
- The fundamental approach involves using formal organised sector data as a proxy for estimating growth in the informal unorganised sector.
- This methodology rests upon a critical assumption that both sectors move in synchronized directions and patterns.
Scale of the Unorganised Sector
- Unorganised sector (excluding agriculture) constitutes approximately 30% of India’s total GDP.
- Such substantial proportion demands highly accurate and reliable estimation methods.
- Economists question whether reliable and accurate measurement mechanisms actually exist for this significant economic component.
- Scale of impact: Errors in estimating 30% of GDP translate into substantial distortions in overall growth calculations.
Key Limitations in Quarterly GDP Estimation and Data Availability
- Quarterly growth estimate of 8.2% generated widespread media enthusiasm, despite lack of comprehensive quarterly data.
- Quarterly GDP estimation relied heavily on assumptions, historical trends, and past relationships.
- Physical correction and real-time data availability for most sectors remained absent.
- Resolution of estimation limitations was stated to be impossible until most required quarterly data were actually collected and corrected.
Why This Methodology Fails: Problem of Assumption
Core Methodological Flaw
- When organised sector performance is utilized as a proxy for unorganised sector calculations, an implicit assumption undergirds the entire approach: that both sectors move uniformly in same directions.
- However, this assumption demonstrates critical failure during periods of crisis or unusual economic developments.
- During such extraordinary circumstances, organised and unorganised sectors diverge significantly, moving in opposite directions rather than synchronized patterns.
Impact of Economic Events
Three major events in recent Indian economic history demonstrated profound misalignment between organised and unorganised sectors:
| Economic Event | Organised Sector Trend | Unorganised Sector Trend | Consequence |
| Demonetisation Impact (2016) | Expanded during this period | Experienced significant decline | Proxy-based calculation methodology overestimated unorganised sector performance. Cash-dependent unorganised businesses faced severe liquidity crises. |
| Goods and Services Tax (GST) Implementation (2017) | Demonstrated expansion through formal documentation | Went into decline due to compliance burdens and transition challenges | Growth calculations became fundamentally distorted due to sectoral divergence. Small and medium informal businesses faced unprecedented administrative burden. |
| COVID-19 Pandemic Consequences (2020-2021) | Continued expansion trajectory through digital adaptation | Faced severe contraction due to lockdowns and economic restrictions | Methodology failed to capture actual economic conditions in informal economy. Millions of informal workers experienced income collapse not reflected in official statistics. |
Expert Assessment of Methodology
- Pronab Sen, former Chief Statistician, and Arun Kumar, former professor at Jawaharlal Nehru University, described methodology as “less than reliable”.
- Primary concern related to assumption that organised and unorganised sectors move in same direction under all conditions.
Way Forward
- Strengthening of data collection systems for unorganized sector must be prioritized through systematic identification, mapping, and continuous tracking of informal economic units, so that dependence on organized sector proxies is gradually reduced and sectoral divergence is adequately captured.
- Expansion of direct surveys and field-level data correction mechanisms must be pursued at regular intervals to ensure that unorganized sector output, employment, and income trends are measured through primary evidence rather than inferred relationships.
- Improvement in availability, granularity, and frequency of quarterly data must be ensured by widening coverage of enterprises and households, so that quarterly GDP estimates are supported by contemporaneous data rather than retrospective assumptions.
- Integration of multiple administrative data sources must be strengthened to supplement survey-based estimates, ensuring better representation of informal sector activity during periods of economic disruption and structural change.
- Institutional mechanisms for real-time data validation and correction must be developed to reduce lag between economic activity and its reflection in national accounts statistics.
- Transparency regarding assumptions, proxies, and estimation limitations used in GDP compilation must be institutionalized, so that policymakers, analysts, and citizens are clearly informed about reliability and uncertainty of headline growth figures.
- Revision of estimation frameworks for crisis and shock periods must be undertaken to account for asymmetric impacts on organized and unorganized sectors, ensuring that exceptional events do not distort long-term growth assessment.
- Statistical capacity of institutions must be enhanced through technical strengthening, methodological innovation, and human resource development, enabling effective handling of complexity and scale of informal economy.
- Coordination between statistical agencies and field-level data collectors must be improved to ensure consistency, comparability, and credibility of national accounts across time periods.
- Independent peer review of national accounts methodology must be encouraged to periodically assess robustness of estimation practices and to align statistical credibility with international standards.
- Media engagement with data quality and methodology issues must be strengthened, ensuring that economic reporting moves beyond headline growth numbers and supports informed public discourse and democratic accountability.
Conclusion
The IMF’s ‘C’ grade signals serious doubts about reliability of India’s national accounts, especially given unorganised sector’s nearly 30% contribution to GDP. Reliance on proxy-based estimation, which breaks down during economic shocks, weakens credibility of growth figures. Beyond base-year revision, sustained credibility requires statistically robust and physically verified measurement of informal economy activity.
